BayesMallows

BayesMallows: Bayesian Preference Learning with the Mallows Rank Model.

Trace Plots from Metropolis-Hastings Algorithm

Assign Assessors to Clusters

Asymptotic Approximation of Partition Function

Beach Preferences

Compute Consensus Ranking

Compute importance sampling estimates of log partition function for footrule and Spearman distances.

Preference Learning with the Mallows Rank Model

Compute Mixtures of Mallows Models

Compute Posterior Intervals

Internal function for generating transitive closure

Estimate Partition Function

Expected value of metrics under a Mallows rank model

Generate Constraint Set from Pairwise Comparisons

Generate Initial Ranking

Generate Transitive Closure

Compute the logarithm of the partition function for a Mallows rank model

Compute the Distance between two rankings

Checking for Label Switching in the Mallows Mixture Model

Likelihood and log-likelihood evaluation for a Mallows mixture model

Compute the logarithm of the expected distance of metrics for a Mallows rank model

Observation frequencies in the Bayesian Mallows model

Plot Posterior Distributions

Plot Within-Cluster Sum of Distances

Plot Top-k Rankings with Pairwise Preferences

True ranking of the weights of 20 potatoes.

Result of ranking potatoes by weight, where the assessors were only allowed to inspected the potatoes visually. 12 assessors ranked 20 potatoes.

Result of ranking potatoes by weight, where the assessors were allowed to lift the potatoes. 12 assessors ranked 20 potatoes.

Predict Top-k Rankings with Pairwise Preferences

Print Method for BayesMallows Objects

Print Method for BayesMallowsMixtures Objects

Convert between ranking and ordering.

Distance between a set of rankings and a given rank sequence

Frequency distribution of the ranking sequences

Sample from the Mallows distribution.

Worker function for computing the posterior distribution.

Random Samples from the Mallows Rank Model

Sushi Rankings

Check if a vector is a permutation